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12:37 PM
@rene Are you sure this is the solution you want?
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Q: Let the default collation of the SEDE databases match the dominant collation used for the nvarchar columns

reneToday I was working on a SEDE query when I was met with a somewhat unexpected error message: Cannot resolve the collation conflict between "SQL_Latin1_General_CP1_CS_AS" and "SQL_Latin1_General_CP1_CI_AS" in the equal to operation. The joy of collation. This is the repro: Create table #foo (bar...

If the databases are created with CS collation, the query in that question will fail in a different way (Invalid object name 'tags'.). And so will go every query in SEDE that isn't carefully made to be case sensitive wrt object and column names.
Unfortunately, I think the answer is going to be to continue to create the databases in CI and just force Posts/PostHistory in StackOverflow to be CS. Which leaves the #temp table joining problem as is (changing the setting won't magically fix any existing queries, it would just make future queries easier). Hopefully recent work has reduced the need for #temp tables a little.
 
1:08 PM
@AaronBertrand That .. is a side-effect I didn't anticipate (nor tested). But yeah, that will not fly in that case. My main concern was to not fail in surprising ways for that query.
@AaronBertrand I'll accept that as answer.
 
 
1 hour later…
2:23 PM
0
A: Let the default collation of the SEDE databases match the dominant collation used for the nvarchar columns

Aaron BertrandWhile this change would solve the issue with joining on #temp table columns, the specific query referenced in the question would still break, but in a different way: Msg 208, Level 16, State 1 Invalid object name 'tags'. This is because the database-level collation enforces how entity names are...

 
2:44 PM
@AaronBertrand thanks! I accepted the answer.
 
 
4 hours later…
6:48 PM
0
Q: Determining the least dangerous tags with over 1K answers to answer

CrSb0001So I thought I'd take another shot at making a SEDE query after being somewhat inactive after a while. Essentially what this does is that it takes the tags that have at least 1000 answers for that tag and ranks them based on the ratio of upvotes to downvotes (you won't get downvoted as often for ...

 

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